CN111597524B - Verification method and system for seal sample sampling personnel - Google Patents
Verification method and system for seal sample sampling personnel Download PDFInfo
- Publication number
- CN111597524B CN111597524B CN202010442505.9A CN202010442505A CN111597524B CN 111597524 B CN111597524 B CN 111597524B CN 202010442505 A CN202010442505 A CN 202010442505A CN 111597524 B CN111597524 B CN 111597524B
- Authority
- CN
- China
- Prior art keywords
- image
- seal
- seal sample
- sample
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 153
- 238000005070 sampling Methods 0.000 title claims abstract description 99
- 238000012795 verification Methods 0.000 title claims abstract description 37
- 238000007689 inspection Methods 0.000 claims abstract description 41
- 238000012545 processing Methods 0.000 claims description 222
- 238000007789 sealing Methods 0.000 claims description 87
- 230000009466 transformation Effects 0.000 claims description 27
- 238000003062 neural network model Methods 0.000 claims description 24
- 238000013135 deep learning Methods 0.000 claims description 18
- 239000000126 substance Substances 0.000 claims description 15
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 230000006698 induction Effects 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000010200 validation analysis Methods 0.000 claims 2
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000013475 authorization Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Collating Specific Patterns (AREA)
Abstract
The invention provides a verification method and a system for a seal sample sampling inspection person, which can perform corresponding identity verification on the seal sample sampling inspection person based on an acquired image in the whole process of the execution of the seal sample sampling inspection.
Description
Technical Field
The invention relates to the technical field of seal sample sampling inspection safety certification, in particular to a verification method and a verification system for seal sample sampling inspection personnel.
Background
The samples are required to be correspondingly sealed before being sent for inspection, and the sealing treatment can involve different operators and different operation procedures, so that the samples can be completely preserved. In the actual operation process, the samples are contacted by different persons in the sample sealing process, which is difficult to ensure that all the operators have authorized operation qualification, and if the samples are subjected to sampling inspection by unauthorized persons in the sample sealing process, the safety risk of the sample sealing process is easily increased. Currently, in order to authenticate the seal sample sampling personnel, the seal sample sampling personnel is generally required to perform corresponding biological information and/or password authentication before the seal sample sampling personnel perform the seal sample sampling, so that the personnel meeting the authorization condition are allowed to perform corresponding seal sample sampling operation. However, this method only performs the verification of the seal sample sampling personnel at the pre-stage of the execution of the seal sample sampling, and it cannot be completely guaranteed that the corresponding sampling personnel also meet the corresponding authorization conditions in the process of the seal sample sampling. It can be seen that, in the prior art, the verification of the seal sample sampling personnel is only limited to the temporary identity verification before the seal sample sampling is performed, and the verification of the whole process is not performed on the seal sample sampling personnel, which seriously reduces the safety and reliability of the seal sample sampling execution.
Disclosure of Invention
The method and the system for verifying the seal sample sampling personnel determine an image acquisition mode related to the seal sample processing process by acquiring seal sample environment information and seal sample related personnel existing state information corresponding to the seal sample processing process, acquire different types of seal sample processing images corresponding to the seal sample processing process according to the image acquisition mode, extract image features related to target personnel objects from the seal sample processing images to obtain corresponding target personnel image features, and perform identity matching processing on the target personnel image features to determine identity information of the seal sample sampling personnel existing in the seal sample processing process; therefore, the verification method and the verification system for the sample sealing and sampling inspection personnel can perform corresponding identity verification on the sample sealing and sampling inspection personnel based on the acquired image in the whole process of sample sealing and sampling inspection execution, can save the trouble that the sample sealing and sampling inspection personnel actively provide personal biological information and/or password information, can effectively improve the intelligence of the whole process of identity verification on the sample sealing and sampling inspection personnel, and improve the safety and the reliability of sample sealing and sampling inspection execution.
The invention provides a verification method for a seal sample sampling and inspecting person, which is characterized by comprising the following steps:
step S1, acquiring seal sample environment information and seal sample related personnel existing state information corresponding to the seal sample processing process so as to determine an image acquisition mode related to the seal sample processing process;
step S2, obtaining different types of seal processing images corresponding to the seal processing process according to the image acquisition mode;
step S3, extracting the image characteristics of the target person object from the seal sample processing image so as to obtain the corresponding image characteristics of the target person;
step S4, identity matching processing is carried out on the image features of the target person, so as to determine the identity information of the seal sample sampling personnel existing in the seal sample processing process;
further, in the step S1, obtaining the seal sample environment information and the seal sample related person existing state information corresponding to the seal sample processing procedure, so as to determine that the image acquisition mode related to the seal sample processing procedure specifically includes,
step S101, determining a three-dimensional space for sample sealing processing corresponding to the sample sealing processing process, and acquiring at least one of brightness information of a sample sealing environment, interval distribution information of the sample sealing environment and shielding state information of the sample sealing environment corresponding to the sample sealing processing process as the sample sealing environment information;
step S102, carrying out infrared scanning induction processing on the sample sealing processing process so as to obtain the number information and/or the position information of operators corresponding to the sample sealing processing process, wherein the number information and/or the position information of the operators are used as the existing state information of the sample sealing related personnel;
step S103, according to the seal sample environment information and the seal sample related personnel existing state information, at least one of monocular image acquisition operation, multi-ocular image acquisition operation, fixed focus image acquisition operation and zoom image acquisition operation is carried out on the seal sample processing process;
further, in the step S2, the obtaining of the different types of seal processing images corresponding to the seal processing procedure according to the image acquisition mode specifically includes,
step S201, performing monocular image acquisition operation and/or multi-view image acquisition operation on the seal sample processing process so as to obtain monocular image data and/or multi-view image data related to the seal sample processing process;
step S202, performing fixed focus image acquisition operation and/or zoom image acquisition operation on the seal sample processing process so as to obtain fixed focus image data and/or zoom image data related to the seal sample processing process;
step S203, acquiring parallax transformation images among different time sequence shooting images in the monocular image data and/or the monocular image data to serve as a part of the seal sample processing images;
step S204, acquiring depth-of-field conversion images among different time sequence shooting images in the fixed-focus image data and/or the zoom image data to serve as a part of the seal sample processing image;
further, in the step S3, the extracting the image features of the seal sample processing image with respect to the target person object to obtain the corresponding image features of the target person specifically includes,
step S301, extracting image parallax features of the parallax transformation images related to the sample sealing processing process in the sample sealing processing images, so as to obtain the parallax image features related to the target person object;
step S302, extracting depth-of-field features of the depth-of-field conversion image related to the seal sample processing process in the seal sample processing image so as to obtain depth-of-field image features related to the target person object;
step S303, respectively carrying out positioning processing on the personnel contour and/or the personnel texture of the target personnel object on the parallax image characteristic and the depth image characteristic so as to obtain corresponding target personnel contour distribution information and/or target personnel texture distribution information;
step S304, carrying out data fitting processing on the target person contour distribution information and/or the target person texture distribution information so as to obtain the target person image characteristics;
further, in step S4, performing identity matching processing on the image features of the target person to determine that the identity information of the seal sampling person existing in the seal sample processing process specifically includes,
step S401, constructing a target person image feature deep learning neural network model related to authorized persons, and optimizing the target person image feature deep learning neural network model;
step S402, performing the identity matching processing on the image feature information of the target person by using the optimized image feature deep learning neural network model of the target person so as to obtain the identity information of the seal sample sampling personnel;
step S403, performing security evaluation processing on the identity information of the seal sample inspector to determine whether the seal sample inspector currently existing in the seal sample processing process is an authorized person, specifically,
step S4031, according to the following formula, the identity information of the authorized personnel is preprocessed,
in the above formula, bijItem j of preprocessed information for the ith authorized person, aijThe jth information of the ith authorized person,
the average value of the ith item of information, m is the number of authorized personnel, and n is the information content of the authorized personnel;
step S4032, according to the following formula, calculating the similarity between the seal sample sampling and inspection personnel and the authorized personnel
In the above formula, wiFor the similarity corresponding to the identity information of the seal sample sampling and inspection personnel and the identity information of the ith authorized personnel, C is the identity information set of the seal sample sampling and inspection personnel, and C ═ C0j)1×n，BiA preprocessed identity information set for the ith authorized person, and Bi＝(bij)1×n,i＝1,2,Λ,m；
Step S4033, judge whether the seal sample sampling personnel belong to authorized personnel,
if it satisfies wiMore than or equal to 95 percent, determining that the specimen sealing sampling personnel belongs to authorized personnel, otherwise determining that the specimen sealing sampling personnel does not belong to authorized personnelTo authorized personnel.
The invention also provides a verification system for the seal sample sampling personnel, which is characterized in that:
the verification system for the seal sample sampling personnel comprises an image acquisition mode determining module, a seal sample processing image acquiring module, a target personnel image characteristic acquiring module and an identity information determining module; wherein the content of the first and second substances,
the image acquisition mode determining module is used for determining an image acquisition mode related to the sample sealing processing process according to the sample sealing environment information corresponding to the sample sealing processing process and the existence state information of the sample sealing related personnel;
the seal sample processing image acquisition module is used for acquiring different types of seal sample processing images corresponding to the seal sample processing process according to the image acquisition mode;
the target person image feature acquisition module is used for extracting image features of the target person object from the seal sample processing image so as to obtain corresponding target person image features;
the identity information determining module is used for performing identity matching processing on the image features of the target person so as to determine the identity information of the sample sealing and sampling personnel existing in the sample sealing processing process;
further, the image acquisition mode determining module comprises a seal sample environment information acquiring submodule, a seal sample related personnel existing state information acquiring submodule and an image acquisition operation determining submodule; wherein the content of the first and second substances,
the seal sample environment information acquisition submodule is used for processing a three-dimensional space according to a seal sample corresponding to the seal sample processing process so as to acquire at least one of seal sample environment brightness information, seal sample environment interval distribution information and seal sample environment shielding state information corresponding to the seal sample processing process, and the at least one of seal sample environment brightness information, seal sample environment interval distribution information and seal sample environment shielding state information is used as the seal sample environment information;
the seal sample related person existing state information acquisition submodule is used for carrying out infrared scanning induction processing on the seal sample processing process so as to acquire the number information and/or the position information of the operators corresponding to the seal sample processing process, and the number information and/or the position information are used as the seal sample related person existing state information;
the image acquisition operation determining submodule is used for carrying out at least one of monocular image acquisition operation, multi-view image acquisition operation, fixed-focus image acquisition operation and zooming image acquisition operation on the seal sample processing process according to the seal sample environment information and the seal sample related personnel existence state information;
further, the seal sample processing image acquisition module comprises a first image data acquisition sub-module, a second image data acquisition sub-module, a first image transformation sub-module and a second image transformation sub-module; wherein the content of the first and second substances,
the first image data acquisition sub-module is used for performing monocular image acquisition operation and/or multi-view image acquisition operation on the seal sample processing process so as to obtain monocular image data and/or multi-view image data related to the seal sample processing process;
the second image data acquisition sub-module is used for performing fixed-focus image acquisition operation and/or zooming image acquisition operation on the seal sample processing process so as to obtain fixed-focus image data and/or zooming image data related to the seal sample processing process;
the first image transformation submodule is used for acquiring parallax transformation images among different time sequence shooting images in the monocular image data and/or the monocular image data to serve as a part of the seal sample processing images;
the second image transformation submodule is used for acquiring depth-of-field transformation images among different time sequence shooting images in the fixed-focus image data and/or the zoom image data to serve as a part of the seal sample processing image;
further, the target personnel image feature acquisition module comprises a parallax image feature acquisition sub-module, a depth image feature acquisition sub-module, a personnel contour and/or texture calculation sub-module and a target personnel image feature fitting sub-module; wherein the content of the first and second substances,
the parallax image feature acquisition sub-module is used for extracting image parallax features of the parallax transformation images related to the sample sealing processing process in the sample sealing processing images so as to obtain the parallax image features related to the target person object;
the depth-of-field image characteristic acquisition submodule is used for extracting the depth-of-field characteristics of the depth-of-field conversion image related to the seal sample processing process in the seal sample processing image so as to obtain the depth-of-field image characteristics related to the target person object;
the person contour and/or texture calculation sub-module is used for respectively carrying out positioning processing on the person contour and/or the person texture of the target person object on the parallax image feature and the depth image feature so as to obtain corresponding target person contour distribution information and/or target person texture distribution information;
the target person image feature fitting submodule is used for performing data fitting processing on the target person contour distribution information and/or the target person texture distribution information so as to obtain the target person image features;
further, the identity information determining module comprises a neural network model constructing and optimizing sub-module, an identity matching sub-module and an authorized person determining sub-module; wherein the content of the first and second substances,
the neural network model building and optimizing submodule is used for building a target person image feature deep learning neural network model related to authorized persons and optimizing the target person image feature deep learning neural network model;
the identity matching submodule is used for carrying out the identity matching processing on the image feature information of the target person by using the optimized image feature deep learning neural network model of the target person so as to obtain the identity information of the seal sample sampling inspection person;
the authorized person determining submodule is used for performing security evaluation processing on the identity information of the seal sample sampling and inspecting person so as to determine whether the seal sample sampling and inspecting person existing in the seal sample processing process is an authorized person.
Compared with the prior art, the verification method and the verification system for the seal sample sampling personnel determine the image acquisition mode related to the seal sample processing process by acquiring the seal sample environment information and the seal sample related personnel existing state information corresponding to the seal sample processing process, acquire different types of seal sample processing images corresponding to the seal sample processing process according to the image acquisition mode, extract the image characteristics related to the target personnel object of the seal sample processing images to obtain the corresponding target personnel image characteristics, and perform identity matching processing on the target personnel image characteristics to determine the identity information of the seal sample sampling personnel existing in the seal sample processing process; therefore, the verification method and the verification system for the sample sealing and sampling inspection personnel can perform corresponding identity verification on the sample sealing and sampling inspection personnel based on the acquired image in the whole process of sample sealing and sampling inspection execution, can save the trouble that the sample sealing and sampling inspection personnel actively provide personal biological information and/or password information, can effectively improve the intelligence of the whole process of identity verification on the sample sealing and sampling inspection personnel, and improve the safety and the reliability of sample sealing and sampling inspection execution.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a verification method for a seal sample sampling person according to the present invention.
Fig. 2 is a schematic structural diagram of a verification system for a seal sample sampling person according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a verification method for a seal sample sampling person according to an embodiment of the present invention. The verification method for the seal sample sampling personnel comprises the following steps:
step S1, acquiring the seal sample environment information and the seal sample related personnel existing state information corresponding to the seal sample processing process, so as to determine the image acquisition mode related to the seal sample processing process;
step S2, according to the image acquisition mode, obtaining different types of seal processing images corresponding to the seal processing process;
step S3, extracting the image characteristics of the target person object from the seal sample processing image, so as to obtain the corresponding image characteristics of the target person;
step S4, performing identity matching processing on the image features of the target person, so as to determine the identity information of the seal sample sampling personnel existing in the seal sample processing process.
Preferably, in the step S1, the obtaining of the seal sample environment information and the seal sample related person existing state information corresponding to the seal sample processing procedure, so as to determine that the image acquisition mode related to the seal sample processing procedure specifically includes,
step S101, determining a three-dimensional space for sample sealing processing corresponding to the sample sealing processing process, and acquiring at least one of brightness information of a sample sealing environment, interval distribution information of the sample sealing environment and shielding state information of the sample sealing environment corresponding to the sample sealing processing process as the sample sealing environment information;
step S102, carrying out infrared scanning induction processing on the sample sealing processing process so as to obtain the number information and/or the position information of the operators corresponding to the sample sealing processing process, wherein the number information and/or the position information of the operators are used as the existing state information of the sample sealing related personnel;
and step S103, performing at least one of monocular image acquisition operation, multi-view image acquisition operation, fixed-focus image acquisition operation and zooming image acquisition operation on the seal sample processing process according to the seal sample environment information and the seal sample related personnel existing state information.
Preferably, in step S2, the obtaining of the different types of seal processing images corresponding to the seal processing procedure according to the image acquisition mode specifically includes,
step S201, performing monocular image acquisition operation and/or multi-view image acquisition operation on the seal sample processing process so as to obtain monocular image data and/or multi-view image data related to the seal sample processing process;
step S202, a fixed focus image acquisition operation and/or a zooming image acquisition operation are/is carried out on the seal sample processing process, so that fixed focus image data and/or zooming image data related to the seal sample processing process are/is obtained;
step S203, acquiring parallax transformation images among the monocular image data and/or the monocular image data and the captured images in different time sequences to serve as a part of the seal sample processing image;
step S204, acquiring a depth-of-field conversion image between the fixed-focus image data and/or the zoom image data with respect to different time-series captured images, as a part of the seal sample processing image.
Preferably, in the step S3, the step of extracting the image features of the seal-processed image with respect to the target person object to obtain the corresponding image features of the target person specifically includes,
step S301, extracting image parallax features of the parallax transformation image related to the sample sealing processing process in the sample sealing processing image so as to obtain parallax image features related to the target person object;
step S302, extracting the depth of field characteristics of the image from the depth of field conversion image in the seal sample processing process, so as to obtain the depth of field image characteristics of the target person object;
step S303, respectively carrying out positioning processing on the personnel contour and/or the personnel texture of the target personnel object on the parallax image characteristic and the depth image characteristic so as to obtain corresponding target personnel contour distribution information and/or target personnel texture distribution information;
step S304, performing data fitting processing on the target person contour distribution information and/or the target person texture distribution information so as to obtain the target person image characteristics.
Preferably, in the step S4, the image feature of the target person is subjected to identity matching processing, so as to determine that the identity information of the seal sampling person existing in the seal sample processing process specifically includes,
step S401, constructing a target person image feature deep learning neural network model related to authorized persons, and optimizing the target person image feature deep learning neural network model;
step S402, carrying out the identity matching processing on the image feature information of the target person by using the optimized image feature deep learning neural network model of the target person so as to obtain the identity information of the seal sample sampling personnel;
step S403, performing security evaluation processing on the identity information of the seal sample inspector to determine whether the seal sample inspector currently existing in the seal sample processing process is an authorized one, specifically,
step S4031, the identity information of the authorized person is preprocessed according to the following formula,
in the above formula, bijItem j of preprocessed information for the ith authorized person, aijThe jth information of the ith authorized person,
the average value of the ith item of information, m is the number of authorized personnel, and n is the information content of the authorized personnel;
step S4032, according to the following formula, calculating the similarity between the sample sealing sampling and inspection personnel and the authorized personnel
In the above formula, wiFor the similarity corresponding to the identity information of the seal sample sampling and inspection personnel and the identity information of the ith authorized personnel, C is the identity information set of the seal sample sampling and inspection personnel, and C ═ C0j)1×n，BiA preprocessed identity information set for the ith authorized person, and Bi＝(bij)1×n,i＝1,2,Λ,m；
Step S4033, determine whether the seal sample sampling personnel belongs to authorized personnel,
if it satisfies wiIf the sample sealing sampling personnel is more than or equal to 95 percent, determining that the sample sealing sampling personnel belongs to authorized personnel, otherwise, determining that the sample sealing sampling personnel does not belong to authorized personnel;
through the process, whether the seal sample sampling and detecting personnel are authorized personnel or not is judged, so that misjudgment can be avoided within a 95% confidence range, and the identity information of the authorized personnel is standardized by preprocessing the identity information of the authorized personnel, so that the judgment result is more accurate.
Fig. 2 is a schematic structural diagram of a verification system for a seal sample sampling and inspection person according to an embodiment of the present invention. The verification system for the seal sample sampling personnel comprises an image acquisition mode determining module, a seal sample processing image acquiring module, a target personnel image characteristic acquiring module and an identity information determining module; wherein the content of the first and second substances,
the image acquisition mode determining module is used for determining an image acquisition mode related to the seal sample processing process according to seal sample environment information corresponding to the seal sample processing process and seal sample related personnel existing state information;
the seal sample processing image acquisition module is used for acquiring different types of seal sample processing images corresponding to the seal sample processing process according to the image acquisition mode;
the target person image feature acquisition module is used for extracting image features of the target person object from the seal sample processing image so as to obtain corresponding target person image features;
the identity information determining module is used for carrying out identity matching processing on the image features of the target person so as to determine the identity information of the sample sealing and sampling personnel existing in the sample sealing processing process.
Preferably, the image acquisition mode determining module comprises a seal sample environment information acquiring submodule, a seal sample related personnel existence state information acquiring submodule and an image acquisition operation determining submodule; wherein the content of the first and second substances,
the seal sample environment information acquisition submodule is used for processing a three-dimensional space according to a seal sample corresponding to the seal sample processing process so as to acquire at least one of seal sample environment brightness information, seal sample environment interval distribution information and seal sample environment shielding state information corresponding to the seal sample processing process as the seal sample environment information;
the seal sample related personnel existing state information acquisition submodule is used for carrying out infrared scanning induction processing on the seal sample processing process so as to acquire the number information and/or the position information of the operators corresponding to the seal sample processing process and take the information as the existing state information of the seal sample related personnel;
the image acquisition operation determining submodule is used for carrying out at least one of monocular image acquisition operation, multi-view image acquisition operation, fixed-focus image acquisition operation and zooming image acquisition operation on the seal sample processing process according to the seal sample environment information and the existence state information of the seal sample related personnel.
Preferably, the seal sample processing image obtaining module comprises a first image data obtaining sub-module, a second image data obtaining sub-module, a first image transformation sub-module and a second image transformation sub-module; wherein the content of the first and second substances,
the first image data acquisition submodule is used for carrying out monocular image acquisition operation and/or multi-view image acquisition operation on the seal sample processing process so as to obtain monocular image data and/or multi-view image data related to the seal sample processing process;
the second image data acquisition submodule is used for carrying out fixed-focus image acquisition operation and/or zooming image acquisition operation on the seal sample processing process so as to obtain fixed-focus image data and/or zooming image data related to the seal sample processing process;
the first image transformation submodule is used for acquiring parallax transformation images among the monocular image data and/or the captured images of different time sequences to serve as a part of the seal sample processing image;
the second image transformation submodule is used for acquiring depth-of-field transformation images among different time sequence shooting images in the fixed-focus image data and/or the zoom image data so as to serve as a part of the seal sample processing image.
Preferably, the target person image feature acquisition module comprises a parallax image feature acquisition sub-module, a depth image feature acquisition sub-module, a person contour and/or texture calculation sub-module and a target person image feature fitting sub-module; wherein the content of the first and second substances,
the parallax image feature acquisition sub-module is used for extracting image parallax features of the parallax transformation image related to the sample sealing processing process in the sample sealing processing image so as to obtain the parallax image features related to the target person object;
the depth-of-field image characteristic acquisition submodule is used for extracting the depth-of-field characteristic of an image of a depth-of-field conversion image related to the seal sample processing process in the seal sample processing image so as to obtain the depth-of-field image characteristic related to the target person object;
the person contour and/or texture calculation sub-module is used for respectively carrying out positioning processing on the person contour and/or the person texture of the target person object on the parallax image feature and the depth image feature so as to obtain corresponding target person contour distribution information and/or target person texture distribution information;
the target person image feature fitting submodule is used for performing data fitting processing on the target person contour distribution information and/or the target person texture distribution information so as to obtain the target person image features.
Preferably, the identity information determination module comprises a neural network model construction and optimization submodule, an identity matching submodule and an authorized person determination submodule; wherein the content of the first and second substances,
the neural network model construction and optimization submodule is used for constructing a target person image feature deep learning neural network model related to authorized persons and optimizing the target person image feature deep learning neural network model;
the identity matching submodule is used for carrying out the identity matching processing on the image feature information of the target person by using the optimized image feature deep learning neural network model of the target person so as to obtain the identity information of the seal sample sampling inspection person;
the authorized person determining submodule is used for performing security evaluation processing on the identity information of the seal sample sampling and inspecting person so as to determine whether the seal sample sampling and inspecting person existing in the seal sample processing process is an authorized person.
As can be seen from the content of the above embodiment, the verification method and system for the seal sample sampling personnel determine the image acquisition mode related to the seal sample processing process by acquiring the seal sample environment information and the seal sample related personnel presence state information corresponding to the seal sample processing process, acquire different types of seal sample processing images corresponding to the seal sample processing process according to the image acquisition mode, perform image feature extraction on the seal sample processing images related to the target personnel object to obtain corresponding target personnel image features, and perform identity matching processing on the target personnel image features to determine the identity information of the seal sample sampling personnel present in the seal sample processing process; therefore, the verification method and the verification system for the sample sealing and sampling inspection personnel can perform corresponding identity verification on the sample sealing and sampling inspection personnel based on the acquired image in the whole process of sample sealing and sampling inspection execution, can save the trouble that the sample sealing and sampling inspection personnel actively provide personal biological information and/or password information, can effectively improve the intelligence of the whole process of identity verification on the sample sealing and sampling inspection personnel, and improve the safety and the reliability of sample sealing and sampling inspection execution.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. The verification method for the seal sample examiner is characterized by comprising the following steps:
step S1, acquiring seal sample environment information and seal sample related personnel existing state information corresponding to the seal sample processing process so as to determine an image acquisition mode related to the seal sample processing process;
step S2, obtaining different types of seal processing images corresponding to the seal processing process according to the image acquisition mode;
step S3, extracting the image characteristics of the target person object from the seal sample processing image so as to obtain the corresponding image characteristics of the target person;
step S4, identity matching processing is carried out on the image features of the target person, so as to determine the identity information of the seal sample sampling personnel existing in the seal sample processing process;
in step S1, obtaining the seal sample environment information and the seal sample related person presence state information corresponding to the seal sample processing procedure, so as to determine that the image acquisition mode related to the seal sample processing procedure specifically includes,
step S101, determining a three-dimensional space for sample sealing processing corresponding to the sample sealing processing process, and acquiring at least one of brightness information of a sample sealing environment, interval distribution information of the sample sealing environment and shielding state information of the sample sealing environment corresponding to the sample sealing processing process as the sample sealing environment information;
step S102, carrying out infrared scanning induction processing on the sample sealing processing process so as to obtain the number information and/or the position information of operators corresponding to the sample sealing processing process, wherein the number information and/or the position information of the operators are used as the existing state information of the sample sealing related personnel;
step S103, according to the seal sample environment information and the seal sample related personnel existing state information, at least one of monocular image acquisition operation, multi-ocular image acquisition operation, fixed focus image acquisition operation and zoom image acquisition operation is carried out on the seal sample processing process;
in step S2, the obtaining of the different types of seal processing images corresponding to the seal processing procedure according to the image acquisition mode specifically includes,
step S201, performing monocular image acquisition operation and/or multi-view image acquisition operation on the seal sample processing process so as to obtain monocular image data and/or multi-view image data related to the seal sample processing process;
step S202, performing fixed focus image acquisition operation and/or zoom image acquisition operation on the seal sample processing process so as to obtain fixed focus image data and/or zoom image data related to the seal sample processing process;
step S203, acquiring parallax transformation images among different time sequence shooting images in the monocular image data and/or the monocular image data to serve as a part of the seal sample processing images;
step S204, acquiring depth-of-field conversion images between different time series captured images in the fixed focus image data and/or the zoom image data to serve as a part of the seal sample processing image.
2. The method of claim 1 for validating a seal sampler as recited in:
in step S3, the extracting the image features of the seal-processed image with respect to the target person object to obtain the corresponding image features of the target person specifically includes,
step S301, extracting image parallax features of the parallax transformation images related to the sample sealing processing process in the sample sealing processing images, so as to obtain the parallax image features related to the target person object;
step S302, extracting depth-of-field features of the depth-of-field conversion image related to the seal sample processing process in the seal sample processing image so as to obtain depth-of-field image features related to the target person object;
step S303, respectively carrying out positioning processing on the personnel contour and/or the personnel texture of the target personnel object on the parallax image characteristic and the depth image characteristic so as to obtain corresponding target personnel contour distribution information and/or target personnel texture distribution information;
step S304, carrying out data fitting processing on the target person contour distribution information and/or the target person texture distribution information so as to obtain the target person image characteristics.
3. The method of claim 1 for validating a seal sampler as recited in:
in step S4, performing identity matching processing on the image features of the target person, so as to determine that the identity information of the seal sample examiner existing in the seal sample processing process specifically includes,
step S401, constructing a target person image feature deep learning neural network model related to authorized persons, and optimizing the target person image feature deep learning neural network model;
step S402, performing the identity matching processing on the image feature information of the target person by using the optimized image feature deep learning neural network model of the target person so as to obtain the identity information of the seal sample sampling personnel;
step S403, performing security evaluation processing on the identity information of the seal sample inspector to determine whether the seal sample inspector currently existing in the seal sample processing process is an authorized person, specifically,
step S4031, according to the following formula, the identity information of the authorized personnel is preprocessed,
in the above formula, bijItem j of preprocessed information for the ith authorized person, aijThe jth information of the ith authorized person,
the average value of the ith item of information, m is the number of authorized personnel, and n is the information content of the authorized personnel;
step S4032, according to the following formula, calculating the similarity between the seal sample sampling and inspection personnel and the authorized personnel
In the above formula, wiFor the similarity corresponding to the identity information of the seal sample sampling and inspection personnel and the identity information of the ith authorized personnel, C is the identity information set of the seal sample sampling and inspection personnel, and C ═ C0j)1×n，BiA preprocessed identity information set for the ith authorized person, and Bi＝(bij)1×n,i＝1,2,Λ,m；
Step S4033, judge whether the seal sample sampling personnel belong to authorized personnel,
if it satisfies wiAnd if the sample sealing sampling inspection personnel is more than or equal to 95 percent, determining that the sample sealing sampling inspection personnel belongs to authorized personnel, otherwise, determining that the sample sealing sampling inspection personnel does not belong to authorized personnel.
4. A verification system for seal sample sampling personnel, characterized in that:
the verification system for the seal sample sampling personnel comprises an image acquisition mode determining module, a seal sample processing image acquiring module, a target personnel image characteristic acquiring module and an identity information determining module; wherein the content of the first and second substances,
the image acquisition mode determining module is used for determining an image acquisition mode related to the sample sealing processing process according to the sample sealing environment information corresponding to the sample sealing processing process and the existence state information of the sample sealing related personnel;
the seal sample processing image acquisition module is used for acquiring different types of seal sample processing images corresponding to the seal sample processing process according to the image acquisition mode;
the target person image feature acquisition module is used for extracting image features of the target person object from the seal sample processing image so as to obtain corresponding target person image features;
the identity information determining module is used for performing identity matching processing on the image features of the target person so as to determine the identity information of the sample sealing and sampling personnel existing in the sample sealing processing process;
the image acquisition mode determining module comprises a seal sample environment information acquiring submodule, a seal sample related personnel existing state information acquiring submodule and an image acquisition operation determining submodule; wherein the content of the first and second substances,
the seal sample environment information acquisition submodule is used for processing a three-dimensional space according to a seal sample corresponding to the seal sample processing process so as to acquire at least one of seal sample environment brightness information, seal sample environment interval distribution information and seal sample environment shielding state information corresponding to the seal sample processing process, and the at least one of seal sample environment brightness information, seal sample environment interval distribution information and seal sample environment shielding state information is used as the seal sample environment information;
the seal sample related person existing state information acquisition submodule is used for carrying out infrared scanning induction processing on the seal sample processing process so as to acquire the number information and/or the position information of the operators corresponding to the seal sample processing process, and the number information and/or the position information are used as the seal sample related person existing state information;
the image acquisition operation determining submodule is used for carrying out at least one of monocular image acquisition operation, multi-view image acquisition operation, fixed-focus image acquisition operation and zooming image acquisition operation on the seal sample processing process according to the seal sample environment information and the seal sample related personnel existence state information;
the seal sample processing image acquisition module comprises a first image data acquisition sub-module, a second image data acquisition sub-module, a first image transformation sub-module and a second image transformation sub-module; wherein the content of the first and second substances,
the first image data acquisition sub-module is used for performing monocular image acquisition operation and/or multi-view image acquisition operation on the seal sample processing process so as to obtain monocular image data and/or multi-view image data related to the seal sample processing process;
the second image data acquisition sub-module is used for performing fixed-focus image acquisition operation and/or zooming image acquisition operation on the seal sample processing process so as to obtain fixed-focus image data and/or zooming image data related to the seal sample processing process;
the first image transformation submodule is used for acquiring parallax transformation images among different time sequence shooting images in the monocular image data and/or the monocular image data to serve as a part of the seal sample processing images;
the second image transformation submodule is used for acquiring depth-of-field transformation images among different time sequence shooting images in the fixed-focus image data and/or the zoom image data to serve as a part of the seal sample processing image.
5. The validation system for seal sampling personnel of claim 4, wherein:
the target personnel image feature acquisition module comprises a parallax image feature acquisition sub-module, a depth image feature acquisition sub-module, a personnel contour and/or texture calculation sub-module and a target personnel image feature fitting sub-module; wherein the content of the first and second substances,
the parallax image feature acquisition sub-module is used for extracting image parallax features of the parallax transformation images related to the sample sealing processing process in the sample sealing processing images so as to obtain the parallax image features related to the target person object;
the depth-of-field image characteristic acquisition submodule is used for extracting the depth-of-field characteristics of the depth-of-field conversion image related to the seal sample processing process in the seal sample processing image so as to obtain the depth-of-field image characteristics related to the target person object;
the person contour and/or texture calculation sub-module is used for respectively carrying out positioning processing on the person contour and/or the person texture of the target person object on the parallax image feature and the depth image feature so as to obtain corresponding target person contour distribution information and/or target person texture distribution information;
and the target person image feature fitting submodule is used for performing data fitting processing on the target person contour distribution information and/or the target person texture distribution information so as to obtain the target person image features.
6. The validation system for seal sampling personnel of claim 4, wherein:
the identity information determination module comprises a neural network model construction and optimization sub-module, an identity matching sub-module and an authorized person determination sub-module; wherein the content of the first and second substances,
the neural network model building and optimizing submodule is used for building a target person image feature deep learning neural network model related to authorized persons and optimizing the target person image feature deep learning neural network model;
the identity matching submodule is used for carrying out the identity matching processing on the image feature information of the target person by using the optimized image feature deep learning neural network model of the target person so as to obtain the identity information of the seal sample sampling inspection person;
the authorized person determining submodule is used for performing security evaluation processing on the identity information of the seal sample sampling and inspecting person so as to determine whether the seal sample sampling and inspecting person existing in the seal sample processing process is an authorized person.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010442505.9A CN111597524B (en) | 2020-05-22 | 2020-05-22 | Verification method and system for seal sample sampling personnel |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010442505.9A CN111597524B (en) | 2020-05-22 | 2020-05-22 | Verification method and system for seal sample sampling personnel |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111597524A CN111597524A (en) | 2020-08-28 |
| CN111597524B true CN111597524B (en) | 2021-03-23 |
Family
ID=72186357
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010442505.9A Active CN111597524B (en) | 2020-05-22 | 2020-05-22 | Verification method and system for seal sample sampling personnel |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111597524B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177452B (en) * | 2021-04-22 | 2024-02-27 | 国网江苏省电力有限公司物资分公司 | Sample sealing method and device based on image processing and radio frequency technology |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110163246A (en) * | 2019-04-08 | 2019-08-23 | 杭州电子科技大学 | The unsupervised depth estimation method of monocular light field image based on convolutional neural networks |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110222486A (en) * | 2019-05-18 | 2019-09-10 | 王�锋 | User ID authentication method, device, equipment and computer readable storage medium |
| CN110443016B (en) * | 2019-07-04 | 2023-10-10 | 深圳壹账通智能科技有限公司 | Information leakage prevention method, electronic device and storage medium |
| CN110517381A (en) * | 2019-08-20 | 2019-11-29 | 一石数字技术成都有限公司 | A kind of visitor management system and method based on face registration mechanism |
-
2020
- 2020-05-22 CN CN202010442505.9A patent/CN111597524B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110163246A (en) * | 2019-04-08 | 2019-08-23 | 杭州电子科技大学 | The unsupervised depth estimation method of monocular light field image based on convolutional neural networks |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111597524A (en) | 2020-08-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108563990B (en) | Certificate authentication method and system based on CIS image acquisition system | |
| CN111881726B (en) | Living body detection method and device and storage medium | |
| CN105701885A (en) | Face identification access control system and implementation method thereof | |
| CN110555931A (en) | Face detection and gate inhibition system device based on deep learning recognition | |
| CN106971159A (en) | A kind of image definition recognition methods, identity identifying method and device | |
| CN111783663A (en) | Algorithm evaluation system and detection method for performance detection of human evidence verification equipment | |
| RU2713876C1 (en) | Method and system for detecting alarm events when interacting with self-service device | |
| CN111209980A (en) | Environment detection method and device, electronic equipment and computer readable storage medium | |
| US20150178544A1 (en) | System for estimating gender from fingerprints | |
| CN110930566A (en) | Safe rechecking method, system and device for vehicles to and from prisons and storable medium | |
| CN111597524B (en) | Verification method and system for seal sample sampling personnel | |
| CN117115881A (en) | Face recognition system based on machine learning | |
| CN112862640A (en) | Digital school zone cloud platform management system | |
| Popov | Use of system diversity as tool for identification | |
| CN116468113A (en) | Living body detection model training method, living body detection method and living body detection system | |
| CN113705428A (en) | Living body detection method and apparatus, electronic device, and computer-readable storage medium | |
| CN115116119A (en) | Face recognition system based on digital image processing technology | |
| CN110852203B (en) | Multi-factor suspicious person identification method based on video feature learning | |
| CN112149089A (en) | Computer login authentication management system based on Internet of things | |
| Senthilkumar et al. | A Novel and Smart Administrative Door Lock and Open System using Face Recognition | |
| CN115424383B (en) | Intelligent access control management system and method | |
| CN115035332B (en) | Antigen detection result authentication method based on image recognition | |
| WO2019078405A1 (en) | Forgery defensive power analysis system for fingerprint recognition device and evaluation method therefor | |
| Sousedikova et al. | The Role of Lie Detection Based System in Controlling Borders | |
| CN118279951A (en) | Automatic face recognition system based on multi-mode data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A verification method and system for sealing sample sampling personnel Effective date of registration: 20220531 Granted publication date: 20210323 Pledgee: Nantong CIC Financing Guarantee Co.,Ltd. Pledgor: JIANGSU HAOHAN INFORMATION TECHNOLOGY Co.,Ltd. Registration number: Y2022980006824 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Date of cancellation: 20230523 Granted publication date: 20210323 Pledgee: Nantong CIC Financing Guarantee Co.,Ltd. Pledgor: JIANGSU HAOHAN INFORMATION TECHNOLOGY Co.,Ltd. Registration number: Y2022980006824 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: A Verification Method and System for Sealing Sample Sampling Personnel Effective date of registration: 20230525 Granted publication date: 20210323 Pledgee: Nantong CIC Financing Guarantee Co.,Ltd. Pledgor: JIANGSU HAOHAN INFORMATION TECHNOLOGY Co.,Ltd. Registration number: Y2023980041781 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Granted publication date: 20210323 Pledgee: Nantong CIC Financing Guarantee Co.,Ltd. Pledgor: JIANGSU HAOHAN INFORMATION TECHNOLOGY Co.,Ltd. Registration number: Y2023980041781 |